Data Science is a burgeoning field at the intersection of mathematics, statistics, computer coding, and critical thinking. It aims to draw insights from large databases available in the modern era through various processing techniques, statistical analysis, mathematical modelling, and data visualization.
Only available as a minor or undergraduate certificate.
At a Glance
At Mount Allison, students in the Data Science program will learn concepts and practices for effective management, distribution, and presentation of data, enabling them to successfully engage with the scope and scale of data resources.
The program is designed to engage and serve the interests of a wide range of students with emphasis on interdisciplinary opportunities and challenges of data science, including ethics and privacy.
Skills in data science are highly transferable across many areas of practice and professions, from physics to health care to social policy.
Course areas in data science include:
- linear algebra
- data analysis
- advanced design and statistical analysis
- data visualization and communication
- data acquisition and organization
- experiential data science
A certificate in Data Management (12 credits)
Covers the theory, ethics, and practice of managing and presenting data resources. The certificate will empower students with tools to advance their work in their own discipline, and to progress to graduate or professional practice.
A certificate in Data Analytics (18 credits)
Covers conceptual approaches to analyses of large-scale data, which presents both challenges and opportunities.
A minor in Data Science (24 credits)
Combines both certificates, along with advanced statistics.
Not sure about the difference between a major, a minor, an honours, and a certificate?
MATH 1311 — Introduction to Data Science
This course emphasizes practical techniques for working with large-scale data and introduces tools and techniques for managing, visualizing, and making sense of data through the use of statistical software. Topics include: descriptive statistics, confidence intervals, regression, and machine learning.
DATA 3001 — Data Visualization and Communication
This course will build on concepts established in MATH 1311 to cover accurate and effective visualization and communication of data to both technically trained audiences and to the wider public. Students will learn to organize diverse data types for efficient static, dynamic, and interactive visual presentations to effectively communicate key messages in multiple formats. Students will learn approaches to high throughput report generation and content updating, with principles of open data and maintaining audit trails from presentation back to source. The course will cover common pitfalls or distortions of data presentation, and principles of visual grammar and accessibility for diverse users.
DATA 3101 — Data Acquisition and Organization
This course will build on MATH 1311 to cover high throughput acquisition and management of data. The course will use diverse data types and formats to illustrate conceptual challenges across disciplines. Technical aspects will include evolving approaches to script-based web scraping, file formats and conversions, data mergers and tidying, meta-data organization and capture. In parallel, the course will cover theoretical and ethical aspects of data curation and access policies, development of best practices for research data management and case studies in secure management of sensitive or private data.(
DATA 4001 — Advanced Experiential Data Science
This capstone course will give upper-year students the opportunity to work on data of interest to them, captured either through direct measurement or through access to open data sets, in order to develop management and analysis competencies applicable to their disciplinary area(s). In this integrated seminar and laboratory, students will regularly present their individual works in progress and discuss specialized advanced topics in data science, as relevant both to broad experiential applications, and to their specific projects. The course will allow students to deeply engage with a self-determined data-handling project within their discipline, cultivate a transferrable skill set, and work in an interdisciplinary, collaborative environment to experience a breadth of different applications and perspectives on data science.
GENS 2431 — Data Analysis
This course develops basic skills in data collection, analysis, and presentation. It introduces basic statistical and hypothesis testing procedures, along with relevant software.
ECON 2701 — Introduction to Econometrics
This course introduces statistical tools for handling data generated in uncontrolled environments and the techniques involved in their use. Topics include estimation and inference of single- and multi-variable regression models, large sample techniques, dummy variables, heteroskedasticity, and an introduction to times series.
COMP 1631 — Introduction to Computer Science
This course provides a broad survey of computer science and an introduction to programming. Topics include: origins of computers, data representation and storage, Boolean algebra, digital logic gates, computer architecture, assemblers and compilers, operating systems, networks and the Internet, theories of computation, and artificial intelligence.
MATH 2321 — Statistics II
This is a second course in the concepts and techniques of probability and statistics. The course covers a selection of topics from analysis of variance, linear and nonlinear regression, correlation estimation and prediction, independence, Wilcoxon and goodness-of-fit tests and includes data analysis using statistical software. Examples come from a wide variety of sources and disciplines.
For a full list of data science courses, visit our Academic Calendar — Data Science.
Dr. Matthew Betti
Assistant professor, Mathematics and Computer Science
» Mount Allison mathematics professor part of National COVID-19 Modelling Task Force
Whether you're entering the job market or continuing your education, your Mount Allison degree will stand out.
Mount Allison has been recognized by Maclean's as the top primarily undergraduate university in Canada more times than any other university.
With experiential learning and career development opportunities available in every degree, you'll also graduate with hands-on learning and on-the-job experience.
Our graduates also boast extraordinarily high acceptance rates to top graduate programs and professional schools such as law and medicine.
Popular career paths in data science include:
- health sciences
- environmental management
- banking and finance
- business analytics
Mount A is #1 in scholarships and bursaries
Maclean’s ranks Mount Allison first in scholarships and bursaries, and second in the number of students who have won national academic awards. To date, 55 Mount Allison students have become Rhodes Scholars.